Biblio

Failure to detect malware at its very inception leaves room for it to post significant threat and cost to cyber security for not only individuals, organizations but also the society and nation. However, the rapid growth in volume and diversity of malware renders conventional detection techniques that utilize feature extraction and comparison insufficient, making it very difficult for well-trained network administrators to identify malware, not to mention regular users of internet. Challenges in malware detection is exacerbated since complexity in the type and structure also increase dramatically in these years to include source code, binary file, shell script, Perl script, instructions, settings and others. Such increased complexity offers a premium on misjudgment. In order to increase malware detection efficiency and accuracy under large volume and multiple types of malware, this research adopts Convolutional Neural Networks (CNN), one of the most successful deep learning techniques. The experiment shows an accuracy rate of over 90% in identifying malicious and benign codes. The experiment also presents that CNN is effective with detecting source code and binary code, it can further identify malware that is embedded into benign code, leaving malware no place to hide. This research proposes a feasible solution for network administrators to efficiently identify malware at the very inception in the severe network environment nowadays, so that information technology personnel can take protective actions in a timely manner and make preparations for potential follow-up cyber-attacks.

With the rapid development of the contemporary society, wide use of smart phone and vehicle sensing devices brings a huge influence on the extensive data collection. Network coding can only provide weak security privacy protection. Aiming at weak secure feature of network coding, this paper proposes an information transfer mechanism, Weak Security Network Coding with Homomorphic Encryption (HE-WSNC), and it is integrated into routing policy. In this mechanism, a movement model is designed, which allows information transmission process under Wi-Fi and Bluetooth environment rather than consuming 4G data flow. Not only does this application reduce the cost, but also improve reliability of data transmission. Moreover, it attracts more users to participate.

Named Data Networking (NDN) intrinsically supports in-network caching and multipath forwarding. The two salient features offer the potential to simultaneously transmit content segments that comprise the requested content from original content publishers and in-network caches. However, due to the complexity of maintaining the reachability information of off-path cached content at the fine-grained packet level of granularity, the multipath forwarding and off-path cached copies are significantly underutilized in NDN so far. Network coding enabled NDN, referred to as NC-NDN, was proposed to effectively utilize multiple on-path routes to transmit content, but off-path cached copies are still unexploited. This work enhances NC-NDN with an On-demand Off-path Cache Exploration based Multipath Forwarding strategy, dubbed as O2CEMF, to take full advantage of the multipath forwarding to efficiently utilize off-path cached content. In O2CEMF, each network node reactively explores the reachability information of nearby off-path cached content when consumers begin to request a generation of content, and maintains the reachability at the coarse-grained generation level of granularity instead. Then the consumers simultaneously retrieve content from the original content publisher(s) and the explored capable off-path caches. Our experimental studies validate that this strategy improves the content delivery performance efficiently as compared to that in the present NC-NDN.

Currently, the most commonly used scheme for identity authentication on the Internet is based on asymmetric cryptography and the use of a centralized model. The centralized model needs a Certificate Authority (CA) as a trusted third party and a trust chain of CA. However, CA-based PKI is weak in the single point of failure and certificate transparency. Our system, called SS-DPKI, propose a public and decentralized PKI system model. We describe a detailed scheme as well as application to use decentralized PKI based secure communication. Our proposal prevents storage overhead on the data size of transactions and provide reasonable certificate verification time.

Public Key Infrastructure (PKI) has been popularized in many scenarios such as e-government applications, enterprises, etc. Due to the construction of PKI system of various regions and departments, there formed a lot of isolated PKI management domains, cross-domain authentication has become a problem that cannot ignored, which also has some traditional solutions such as cross-authentication, trust list, etc. However, some issues still exist, which hinder the popularity of unified trust services. For example, lack of unified cross domain standard, the update period of Certificate Revocation List (CRL) is too long, which affects the security of cross-domain authentication. In this paper, we proposed a trust transferring model by using blockchain consensus instead of traditional trusted third party for e-government applications. We exploit how to solve the unified trust service problem of PKI at the national level through consensus and transfer some CA management functions to the blockchain. And we prove the scheme's feasibility from engineering perspective. Besides, the scheme has enough scalability to satisfy trust transfer requirements of multiple PKI systems. Meanwhile, the security and efficiency are also guaranteed compared with traditional solutions.

Physical Unclonable Functions (PUFs) are considered as an attractive low-cost security anchor. The unique features of PUFs are dependent on the Nanoscale variations introduced during the manufacturing variations. Most PUFs exhibit an unreliability problem due to aging and inherent sensitivity to the environmental conditions. As a remedy to the reliability issue, helper data algorithms are used in practice. A helper data algorithm generates and stores the helper data in the enrollment phase in a secure environment. The generated helper data are used then for error correction, which can transform the unique feature of PUFs into a reproducible key. The key can be used to encrypt secret data in the security scheme. In contrast, this work shows that the fuzzy PUFs can be used to secret important data directly by an error-tolerant protocol without the enrollment phase and error-correction algorithm. In our proposal, the secret data is locked in a vault leveraging the unique fuzzy pattern of PUF. Although the noise exists, the data can then be released only by this unique PUF. The evaluation was performed on the most prominent intrinsic PUF - DRAM PUF. The test results demonstrate that our proposal can reach an acceptable reconstruction rate in various environment. Finally, the security analysis of the new proposal is discussed.

Today, Smartphone is a necessary device for people connected to the Internet world. But user privacy and security are still playing important roles in the usage of mobile devices. The user was asked to enter related characters, numbers or drawing a simple graphic on the touch screen as passwords for unlocking the screensaver. Although it could provide the user with a simple and convenient security authentication mechanism, the process is hard to protect against the privacy information leakage under the strict security policy. Nowadays, various keypad lock screen Apps usually provides different type of schemes in unlocking the mobile device screen, such as simple-customized pattern, swipe-to-unlock with a static image and so on. But the vulnerability could provide a chance to hijacker to find out the leakage of graphic pattern information that influences in user information privacy and security.This paper proposes a new graphic pattern authentication mechanism to enhance the strength of that in the keypad lock screen Apps. It integrates random digital graphics and handwriting graphic input track recognition technologies to provide better and more diverse privacy protection and reduce the risk of vulnerability. The proposed mechanism is based on two factor identification scheme. First of all, it randomly changes digital graphic position based on unique passwords every time to increase the difficulty of the stealer's recording. Second, the input track of handwriting graphics is another identification factor for enhancing the complex strength of user authentication as well.

Named Data Networking (or NDN) represents a potential new approach to the current host based Internet architecture which prioritize content over the communication between end nodes. NDN relies on caching functionalities and local data storage, such as a content request could be satisfied by any node holding a copy of the content in its storage. Due to the fact that users in the same network domain can share their cached content with each other and in order to reduce the transmission cost for obtaining the desired content, a cooperative network coding mechanism is proposed in this paper. We first formulate our optimal coding and homomorphic signature scheme as a MIP problem and we show how to leverage Software Defined Networking to provide seamless implementation of the proposed solution. Evaluation results demonstrate the efficiency of the proposed coding scheme which achieves better performance than conventional NDN with random coding especially in terms of transmission cost and security.

We revisit Physical-Layer Network Coding (PLNC) and the reasons preventing it from becoming a staple in wireless networks. We identify its strong coupling to the Two-Way Relay Channel (TWRC) as key among them due to its requiring crossing traffic flows and two-hop node coordination. We introduce SE-PLNC, a Source-Encoded PLNC scheme that is traffic pattern independent and involves coordination only among one-hop neighbors, making it significantly more practical to adopt PLNC in multi-hop wireless networks. To accomplish this, SE-PLNC introduces three innovations: it combines bit-level with physical-level network coding, it shifts most of the coding burden from the relay to the source of the PLNC scheme, and it leverages multi-path relaying opportunities available to a particular traffic flow. We evaluate SE-PLNC using theoretical analysis, proof-of-concept implementation on a Universal Software Radio Peripherals (USRP) testbed, and simulations. The theoretical analysis shows the scalability of SE-PLNC and its efficiency in large ad-hoc networks while the testbed experiments its real-life feasibility. Large-scale simulations show that TWRC PLNC barely boosts network throughput while SE-PLNC improves it by over 30%.

The traditional security system requires a lot of manpower, and the wireless security system has been developed to reduce costs. However, for wireless systems, stability and reliability are important system indicators. In order to effectively improve these two indicators, we have imported butterfly network coding algorithm into the wireless sensing network. Because this algorithm enables each node to play multiple roles, such as routing, encoding, decoding, sending and receiving, it can also improve the throughput of network transmission, and effectively improve the stability and reliability of the wireless security system. This paper used the Wi-Fi module to implement the butterfly network coding algorithm, and is actually installed in the building. The basis for transmission and reception of all nodes in the network is received signal strength indication (RSSI). On the other hand, this is an IoT system for security monitoring.

This paper focuses on research of a provably secure code-based pseudorandom sequence generators whose cryptanalysis problem equals to syndrome decoding (belonging to the NP-complex class). It was found that generated sequences of such well-known Fischer-Stern code-based generator don’t have a maximum period, the actual period is much lower than expected. In our work, we have created a new generator scheme. It retains all advantages of the Fisher-Stern algorithm and provides pseudorandom sequences which are formed with maximum period. Also comparative analysis of proposed generator and popular generators was conducted.

Transient execution attacks like Spectre, Meltdown and Foreshadow have shown that combinations of microarchitectural side-channels can be synergistically exploited to create side-channel leaks that are greater than the sum of their parts. While both hardware and software mitigations have been proposed against these attacks, provable security has remained elusive. This paper introduces a formal methodology for enabling secure speculative execution on modern processors. We propose a new class of information flow security properties called trace property-dependent observational determinism (TPOD). We use this class to formulate a secure speculation property. Our formulation precisely characterises all transient execution vulnerabilities. We demonstrate its applicability by verifying secure speculation for several illustrative programs.

A smart environment is a physical space where devices are connected to provide continuous support to individuals and make their life more comfortable. For this purpose, a smart environment collects, stores, and processes a massive amount of personal data. In general, service providers collect these data according to their privacy policies. To enhance the privacy control, individuals can explicitly express their privacy preferences, stating conditions on how their data have to be used and managed. Typically, privacy checking is handled through the hard matching of users' privacy preferences against service providers' privacy policies, by denying all service requests whose privacy policies do not fully match with individual's privacy preferences. However, this hard matching might be too restrictive in a smart environment because it denies the services that partially satisfy the individual's privacy preferences. To cope with this challenge, in this paper, we propose a soft privacy matching mechanism, able to relax, in a controlled way, some conditions of users' privacy preferences such to match with service providers' privacy policies. At this aim, we exploit machine learning algorithms to build a classifier, which is able to make decisions on future service requests, by learning which privacy preference components a user is prone to relax, as well as the relaxation tolerance. We test our approach on two realistic datasets, obtaining promising results.

Proper evaluation of classifier predictive models requires the selection of appropriate metrics to gauge the effectiveness of a model's performance. The Area Under the Receiver Operating Characteristic Curve (AUC) has become the de facto standard metric for evaluating this classifier performance. However, recent studies have suggested that AUC is not necessarily the best metric for all types of datasets, especially those in which there exists a high or severe level of class imbalance. There is a need to assess which specific metrics are most beneficial to evaluate the performance of highly imbalanced big data. In this work, we evaluate the performance of eight machine learning techniques on a severely imbalanced big dataset pertaining to the cyber security domain. We analyze the behavior of six different metrics to determine which provides the best representation of a model's predictive performance. We also evaluate the impact that adjusting the classification threshold has on our metrics. Our results find that the C4.5N decision tree is the optimal learner when evaluating all presented metrics for severely imbalanced Slow HTTP DoS attack data. Based on our results, we propose that the use of AUC alone as a primary metric for evaluating highly imbalanced big data may be ineffective, and the evaluation of metrics such as F-measure and Geometric mean can offer substantial insight into the true performance of a given model.

We present a new consensus protocol for Blockchain ecosystems - PPoM - Predictive Proof of Metrics. First, we describe the motivation for PPoM - why we need it. Then, we outline its architecture, components, and operation. As part of this, we detail our reputation and reward based approach to bring about consensus in the Blockchain. We also address security and scalability for a PPoM based Blockchain, and discuss potential improvements for future work. Finally, we present measurements for our short term Provider Prediction engine.

With the increasing of health awareness, the users become more and more interested in their daily health information and healthcare activities results from healthcare organizations. They always try to collect them together for better usage. Traditionally, the healthcare data is always delivered by paper format from the healthcare organizations, and it is not easy and convenient for data usage and management. They would have to translate these data on paper to digital version which would probably introduce mistakes into the data. It would be necessary if there is a secure and convenient method for electronic health data transferring between the users and the healthcare organizations. However, for the security and privacy problems, almost no healthcare organization provides a stable and full service for health data delivery. In this paper, we propose a secure and convenient method, QRStream, which splits original health data and loads them onto QR code frame streaming for the data transferring. The results shows that QRStream can transfer text health data smoothly with an acceptable performance, for example, transferring 10K data in 10 seconds.

As steganography is the main practice of hidden writing, many deep neural networks are proposed to conceal secret information into images, whose invisibility and security are unsatisfactory. In this paper, we present an encoder-decoder framework with an adversarial discriminator to conceal messages or images into natural images. The message is embedded into QR code first which significantly improves the fault-tolerance. Considering the mean squared error (MSE) is not conducive to perfectly learn the invisible perturbations of cover images, we introduce a texture-based loss that is helpful to hide information into the complex texture regions of an image, improving the invisibility of hidden information. In addition, we design a truncated layer to cope with stego image distortions caused by data type conversion and a moment layer to train our model with varisized images. Finally, our experiments demonstrate that the proposed model improves the security and visual quality of stego images.

Based on the Indonesian of Statistics the level of society people in 2019 is grow up. Based on data, the bank conducted a community to simple transaction payment in the market. Bank just used a debit card or credit card for the transaction, but the banks need more investment for infrastructure and very expensive. Based on that cause the bank needs another solution for low-cost infrastructure. Obtained from solutions that, the bank implementation QR Code Biometric authentication Payment Online is one solution that fulfills. This application used for payment in online merchant. The transaction permits in this study lie in the biometric encryption, or decryption transaction permission and QR Code Scan to improve communication security and transaction data. The test results of implementation Biometric Cloud Authentication Platform show that AES 256 agents can be implemented for face biometric encryption and decryption. Code Scan QR to carry out transaction permits with Face verification transaction permits gets the accuracy rate of 95% for 10 sample people and transaction process gets time speed of 53.21 seconds per transaction with a transaction sample of 100 times.

Hard-coded credentials such as clear text log-in id and password provided by the IoT manufacturers and unsecured ways of remotely accessing IoT devices are the major security concerns of industry and academia. Limited memory, power, and processing capabilities of IoT devices further worsen the situations in improving the security of IoT devices. In such scenarios, a lightweight security algorithm up to some extent can minimize the risk. This paper proposes one such approach using Quick Response (QR) code to mitigate hard-coded credentials related attacks such as Mirai malware, wreak havoc, etc. The QR code based approach provides non-clear text unpredictable login id and password. Further, this paper also proposes a secured way of remotely accessing IoT devices through modified https. The proposed algorithms are implemented and verified using Raspberry Pi 3 model B.

Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of data, scientists' expertise, tools, and workflows. If data are not tracked properly during the lifecycle, it becomes unfeasible to recreate a ML model from scratch or to explain to stackholders how it was created. The main limitation of provenance tracking solutions is that they cannot cope with provenance capture and integration of domain and ML data processed in the multiple workflows in the lifecycle, while keeping the provenance capture overhead low. To handle this problem, in this paper we contribute with a detailed characterization of provenance data in the ML lifecycle in CSE; a new provenance data representation, called PROV-ML, built on top of W3C PROV and ML Schema; and extensions to a system that tracks provenance from multiple workflows to address the characteristics of ML and CSE, and to allow for provenance queries with a standard vocabulary. We show a practical use in a real case in the O&G industry, along with its evaluation using 239,616 CUDA cores in parallel.

Bare-metal microcontrollers are a family of Internet of Things (IoT) devices which are increasingly deployed in critical industrial environments. Similar to other IoT devices, bare-metal microcontrollers are vulnerable to memory corruption and code-reuse attacks. We propose MicroGuard, a novel mitigation method based on component-level sandboxing and automated code randomization to securely encapsulate application components in isolated environments. We implemented MicroGuard and evaluated its efficacy and efficiency with a real-world benchmark against different types of attacks. As our evaluation shows, MicroGuard provides better security than ACES, current state-of-the-art protection framework for bare-metal microcontrollers, with a comparable performance overhead.

This paper presents the Helix SandBox, an open platform for quick prototyping of smart environment applications. Its architecture was designed to be a lightweight solution that aimed to simplify the instance integration and setup of the main Generic Enablers provided in the FIWARE architecture. As a Powered by FIWARE platform, the SandBox operates with the NGSI standard for interoperability between systems. The platform offers a container-based multicloud architecture capable of running in public, private and bare metal clouds or even in the leading hypervisors available. This paper also proposes a multi-layered architecture capable of integrates the cloud, fog, edge and IoT layers through the federation concept. Lastly, we present two Smart Cities applications conducted in the form of Proof of Concept (PoC) that use the Helix SandBox platform as back-end.

Java is a safe programming language by providing bytecode verification and enforcing memory protection. For instance, programmers cannot directly access the memory but have to use object references. Yet, the Java runtime provides an Unsafe API as a backdoor for the developers to access the low- level system code. Whereas the Unsafe API is designed to be used by the Java core library, a growing community of third-party libraries use it to achieve high performance. The Unsafe API is powerful, but dangerous, which leads to data corruption, resource leaks and difficult-to-diagnose JVM crash if used improperly. In this work, we study the Unsafe crash patterns and propose a memory checker to enforce memory safety, thus avoiding the JVM crash caused by the misuse of the Unsafe API at the bytecode level. We evaluate our technique on real crash cases from the openJDK bug system and real-world applications from AJDK. Our tool reduces the efforts from several days to a few minutes for the developers to diagnose the Unsafe related crashes. We also evaluate the runtime overhead of our tool on projects using intensive Unsafe operations, and the result shows that our tool causes a negligible perturbation to the execution of the applications.

Much recent work focuses on finding bugs and security vulnerabilities in smart contracts written in existing languages. Although this approach may be helpful, it does not address flaws in the underlying programming language, which can facilitate writing buggy code in the first place. We advocate a re-thinking of the blockchain software engineering tool set, starting with the programming language in which smart contracts are written. In this paper, we propose and justify requirements for a new generation of blockchain software development tools. New tools should (1) consider users' needs as a primary concern; (2) seek to facilitate safe development by detecting relevant classes of serious bugs at compile time; (3) as much as possible, be blockchain-agnostic, given the wide variety of different blockchain platforms available, and leverage the properties that are common among blockchain environments to improve safety and developer effectiveness.

For the task with complicated manipulation in unstructured environments, traditional hand-coded methods are ineffective, while reinforcement learning can provide more general and useful policy. Although the reinforcement learning is able to obtain impressive results, its stability and reliability is hard to guarantee, which would cause the potential safety threats. Besides, the transfer from simulation to real-world also will lead in unpredictable situations. To enhance the safety and reliability of robots, we introduce the force and haptic perception into reinforcement learning. Force and tactual sensation play key roles in robotic dynamic control and human-robot interaction. We demonstrate that the force-based reinforcement learning method can be more adaptive to environment, especially in sim-to-real transfer. Experimental results show in object pushing task, our strategy is safer and more efficient in both simulation and real world, thus it holds prospects for a wide variety of robotic applications.